Modeling the origin of urban-output scaling laws

Yang, Vicky Chuqiao; Papachristos, Andrew V.; Abrams, Daniel M.

doi:10.1103/physreve.100.032306

Cited by 23 publications

(24 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These discoveries provide an exciting context for developing a testable and predictive general science of cities. A number of theoretical models have been proposed as origins to superlinear scaling behaviours across cities [16,[19][20][21][22], but only some of these predictions actually give detailed predictions for exponent values. The proposed mechanisms relate social interactions driven by functional complementarities (e.g.…”

Section: Introductionmentioning

confidence: 99%

The interpretation of urban scaling analysis in time

Bettencourt

Yang

Lobo

et al. 2020

J. R. Soc. Interface.

View full text Add to dashboard Cite

Scaling is a general analytical framework used by many disciplines—from physics to biology and the social sciences—to characterize how population-averaged properties of a collective vary with its size. The observation of scale invariance over some range identifies general system types, be they ideal gases, ecosystems or cities. The use of scaling in the analysis of cities quantifies many of their arguably fundamental general characteristics, especially their capacity to create interrelated economies of scale in infrastructure and increasing returns to scale in socio-economic activities. However, the measurement of these effects, and the relationship of observable parameters to theory, hinge on how scaling analysis is used empirically. Here, we show how two equivalent approaches to urban scaling—cross-sectional and temporal—lead to the measurement of different mixtures of the same fundamental parameters describing pure scale and pure temporal phenomena. Specifically, temporal exponents are sensitive to the intensive growth of urban quantities and to circumstances when population growth vanishes, leading to instabilities and infinite divergences. These spurious effects are avoided in cross-sectional scaling, which is more common and closer to theory in terms of quantitative testable expectations for its parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

The interpretation of urban scaling analysis in time

Bettencourt

Yang

Lobo

et al. 2020

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…Understanding the underlying mechanisms of why inequality is systematically scaling with city size is of great future interest with many potential implications. Urban scaling theory in general proposes densifying interactions within cities as the fundamental process leading to the superlinear increase of many features [3,9,10,30,31]. Our analysis shows that the superlinear scaling is not seen within all subsections of the city.…”

Section: Discussionmentioning

confidence: 84%

“…These relationships can often be described by urban outputs, the scaling exponent β is greater than 1, suggesting greater rates of productivity (in both the positive and negative sense) in more populated cities. These observations, known as urban scaling, suggest that a small set of mechanisms significantly influence a variety of urban features across diverse cities [9,10]. Understanding these mechanisms has important implications for developing more prosperous and safer cities.…”

Section: Introductionmentioning

confidence: 99%

Scaling of urban income inequality in the USA

Mora

Heine

Jackson

et al. 2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

Urban scaling analysis, the study of how aggregated urban features vary with the population of an urban area, provides a promising framework for discovering commonalities across cities and uncovering dynamics shared by cities across time and space. Here, we use the urban scaling framework to study an important, but under-explored feature in this community—income inequality. We propose a new method to study the scaling of income distributions by analysing total income scaling in population percentiles. We show that income in the least wealthy decile (10%) scales close to linearly with city population, while income in the most wealthy decile scale with a significantly superlinear exponent. In contrast to the superlinear scaling of total income with city population, this decile scaling illustrates that the benefits of larger cities are increasingly unequally distributed. For the poorest income deciles, cities have no positive effect over the null expectation of a linear increase. We repeat our analysis after adjusting income by housing cost, and find similar results. We then further analyse the shapes of income distributions. First, we find that mean, variance, skewness and kurtosis of income distributions all increase with city size. Second, the Kullback–Leibler divergence between a city’s income distribution and that of the largest city decreases with city population, suggesting the overall shape of income distribution shifts with city population. As most urban scaling theories consider densifying interactions within cities as the fundamental process leading to the superlinear increase of many features, our results suggest this effect is only seen in the upper deciles of the cities. Our finding encourages future work to consider heterogeneous models of interactions to form a more coherent understanding of urban scaling.

show abstract

“…Bettencourt et al (2007) showed that serious crime in the United States exhibits superlinear scaling with exponent β ≈ 1.16 , and some evidence has confirmed similar superlinearity for homicides in Brazil, Colombia, and Mexico (Alves et al, 2013b;Gomez-Lievano et al, 2012). Previous works have also found that different kinds of crime in the United Kingdom and in the United States present nonlinear scaling relationships (Chang et al, 2019;Hanley et al, 2016;Yang et al, 2019). Remarkably, the existence of these scaling laws of crime suggests fundamental urban processes that relate to crime, independent of cities' particularities.…”

Section: More Crime In Cities?mentioning

confidence: 84%

“…Despite this inadequacy, we only have a limited understanding of the impact of nonlinearity on crime rates. Although previous works have investigated population-crime relationships extensively (Alves et al, 2013a;Bettencourt et al 2010;Chang et al 2019;Gomez-Lievano et al, 2012;Hanley et al, 2016;Yang et al, 2019), they have failed to quantify the impact of nonlinear relationships on rankings and restricted their analyses to either specific offenses or countries. The lack of comprehensive systematic studies has limited our knowledge on how the linear assumption influences crime analyses and, more critically, has prevented us from better understanding the effect of population on crime.…”

Section: Introductionmentioning

confidence: 99%

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

Oliveira

2021

Crime Sci

View full text Add to dashboard Cite

Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a region. In this paper, we demonstrate that using per capita rates to rank cities can produce substantially different rankings from rankings adjusted for population size. We analyze the population–crime relationship in cities across 12 countries and assess the impact of per capita measurements on crime analyses, depending on the type of offense. In most countries, we find that theft increases superlinearly with population size, whereas burglary increases linearly. Our results reveal that per capita rankings can differ from population-adjusted rankings such that they disagree in approximately half of the top 10 most dangerous cities in the data analyzed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.

show abstract

Modeling the origin of urban-output scaling laws

Cited by 23 publications

References 49 publications

The interpretation of urban scaling analysis in time

The interpretation of urban scaling analysis in time

Scaling of urban income inequality in the USA

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

Contact Info

Product

Resources

About